

import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if "cls_token" in name:
						a_						:						int 							= name.replace("""cls_token"""  ,	"""vit.embeddings.cls_token""")
			if "mask_token" in name:
						a_						:						List[str] 							= name.replace("""mask_token"""  ,	"""decoder.mask_token""")
			if "decoder_pos_embed" in name:
						a_						:						Optional[int] 							= name.replace("""decoder_pos_embed"""  ,	"""decoder.decoder_pos_embed""")
			if "pos_embed" in name and "decoder" not in name:
						a_						:						Union[str, Any] 							= name.replace("""pos_embed"""  ,	"""vit.embeddings.position_embeddings""")
			if "patch_embed.proj" in name:
						a_						:						Union[str, Any] 							= name.replace("""patch_embed.proj"""  ,	"""vit.embeddings.patch_embeddings.projection""")
			if "patch_embed.norm" in name:
						a_						:						str 							= name.replace("""patch_embed.norm"""  ,	"""vit.embeddings.norm""")
			if "decoder_blocks" in name:
						a_						:						Optional[int] 							= name.replace("""decoder_blocks"""  ,	"""decoder.decoder_layers""")
			if "blocks" in name:
						a_						:						Dict 							= name.replace("""blocks"""  ,	"""vit.encoder.layer""")
			if "attn.proj" in name:
						a_						:						Optional[Any] 							= name.replace("""attn.proj"""  ,	"""attention.output.dense""")
			if "attn" in name:
						a_						:						Any 							= name.replace("""attn"""  ,	"""attention.self""")
			if "norm1" in name:
						a_						:						int 							= name.replace("""norm1"""  ,	"""layernorm_before""")
			if "norm2" in name:
						a_						:						List[Any] 							= name.replace("""norm2"""  ,	"""layernorm_after""")
			if "mlp.fc1" in name:
						a_						:						List[Any] 							= name.replace("""mlp.fc1"""  ,	"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_						:						Optional[int] 							= name.replace("""mlp.fc2"""  ,	"""output.dense""")
			if "decoder_embed" in name:
						a_						:						List[Any] 							= name.replace("""decoder_embed"""  ,	"""decoder.decoder_embed""")
			if "decoder_norm" in name:
						a_						:						Dict 							= name.replace("""decoder_norm"""  ,	"""decoder.decoder_norm""")
			if "decoder_pred" in name:
						a_						:						str 							= name.replace("""decoder_pred"""  ,	"""decoder.decoder_pred""")
			if "norm.weight" in name and "decoder" not in name:
						a_						:						Optional[int] 							= name.replace("""norm.weight"""  ,	"""vit.layernorm.weight""")
			if "norm.bias" in name and "decoder" not in name:
						a_						:						str 							= name.replace("""norm.bias"""  ,	"""vit.layernorm.bias""")

			return name


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for key in orig_state_dict.copy().keys():
						a_						:						Optional[Any] 							= orig_state_dict.pop(a__)

						if "qkv" in key:
									a_						:						List[Any] 							= key.split(""".""")
									a_						:						str 							= int(key_split[1])
									if "decoder_blocks" in key:
												a_						:						Optional[int] 							= config.decoder_hidden_size
												a_						:						Dict 							= """decoder.decoder_layers."""
												if "weight" in key:
															a_						:						Dict 							= val[:dim, :]
															a_						:						List[Any] 							= val[dim : dim * 2, :]
															a_						:						Optional[Any] 							= val[-dim:, :]
												elif "bias" in key:
															a_						:						Optional[int] 							= val[:dim]
															a_						:						Optional[int] 							= val[dim : dim * 2]
															a_						:						int 							= val[-dim:]
									else:
												a_						:						Tuple 							= config.hidden_size
												a_						:						List[Any] 							= """vit.encoder.layer."""
												if "weight" in key:
															a_						:						Tuple 							= val[:dim, :]
															a_						:						Any 							= val[dim : dim * 2, :]
															a_						:						List[str] 							= val[-dim:, :]
												elif "bias" in key:
															a_						:						int 							= val[:dim]
															a_						:						str 							= val[dim : dim * 2]
															a_						:						int 							= val[-dim:]

						else:
									a_						:						int 							= val

			return orig_state_dict


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						List[Any] 							= ViTMAEConfig()
			if "large" in checkpoint_url:
						a_						:						Tuple 							= 1_0_2_4
						a_						:						Optional[Any] 							= 4_0_9_6
						a_						:						List[str] 							= 2_4
						a_						:						str 							= 1_6
			elif "huge" in checkpoint_url:
						a_						:						int 							= 1_4
						a_						:						int 							= 1_2_8_0
						a_						:						Optional[Any] 							= 5_1_2_0
						a_						:						Optional[int] 							= 3_2
						a_						:						Any 							= 1_6

			a_						:						Tuple 							= ViTMAEForPreTraining(a__)

			a_						:						Optional[Any] 							= torch.hub.load_state_dict_from_url(a__  ,	map_location="""cpu""")["""model"""]

			a_						:						Optional[Any] 							= ViTMAEImageProcessor(size=config.image_size)

			a_						:						List[str] 							= convert_state_dict(a__  ,	a__)

			model.load_state_dict(a__)
			model.eval()

			a_						:						Optional[int] 							= """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

			a_						:						str 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			a_						:						int 							= ViTMAEImageProcessor(size=config.image_size)
			a_						:						Optional[Any] 							= image_processor(images=a__  ,	return_tensors="""pt""")

			# forward pass
			torch.manual_seed(2)
			a_						:						Any 							= model(**a__)
			a_						:						Union[str, Any] 							= outputs.logits

			if "large" in checkpoint_url:
						a_						:						Optional[Any] 							= torch.tensor(
						    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]])
			elif "huge" in checkpoint_url:
						a_						:						Tuple 							= torch.tensor(
						    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]])
			else:
						a_						:						Dict 							= torch.tensor(
						    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]])

			# verify logits
			assert torch.allclose(logits[0, :3, :3]  ,	a__  ,	atol=1e-4)

			print(f'''Saving model to {pytorch_dump_folder_path}''')
			model.save_pretrained(a__)

			print(f'''Saving image processor to {pytorch_dump_folder_path}''')
			image_processor.save_pretrained(a__)


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
	    type=str,
	    help="""URL of the checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)

	__snake_case				:      List[Any]   		=			parser.parse_args()
	convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return x if y == 0 else greatest_common_divisor(a__  ,	x % y)


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return (x * y) // greatest_common_divisor(a__  ,	a__)


def 					_UpperCAmelCase				(						a__ = 2_0):

			'''simple docstring'''



			a_						:						Any 							= 1
			for i in range(1  ,	n + 1):
						a_						:						Tuple 							= lcm(a__  ,	a__)
			return g


if __name__ == "__main__":
	print(F"""{solution() = }""")


